RNASeqWorkflow : RNA-Seq workflow for one independent variable analysis

Author: Kuan-Hao Chao (ntueeb05howard@gmail.com)

Abstract

An RNA-Seq workflow for one independent variable analysis

Contents

!!! Coding style ~ 我有盡量檢查了，但是真的好多Ｑ我怕我有沒有按照的地方，學姊可以幫我注意一下～ code, file suffix, value, parameter ‘file directory’ ‘operating system’ Normal software name

1 Introduction

RNA-Seq(RNA sequencing) uses next-generation sequencing(NGS) to provide transcriptome profiling. It has changed our thought about how complex eukaryotic transcriptomes are(Mortazavi et al. 2008).(RNA-Seq Blog). This technique is widely used to look at alternative gene spliced transcripts, post-transcriptional modification, SNPs over time, differences in gene expression in different groups or treatment etc.(wiki). Among them, differential expressed gene(DEG) analysis is the most common downstream analysis.

RNASeqWorkflow provides an easier and R-based approach for running end-to-end two-group RNA-Seq analysis from quality assessment, trimming to downstream differential expression analysis, functional, pathway analysis. Some important features include automated workflow, comprehensive visulization reports, organized, extendable file structure and supporting background process in each step etc.

The following are main tools that are used in this package: ‘HISAT2’ for reads alignment(Kim et al. 2015); ‘StringTie’ for alignments assembly and transcripts quantification(Pertea et al. 2015); ‘Gffcompare’ for comparing merged GTF file with reference GTF file; ‘systemPipeR’ package for quality assessment(Backman et al. 2016); ‘ShortRead’ package for quality trimming(Morgan et al. 2009); ‘ballgown’ package(Fu et al. 2018), ‘TPM & Student’s t-test’, ‘DESeq2’ package(Love et al. 2014) and ‘edgeR’ package(Robinson et al. 2010;McCarthy et al. 2012) for finding potential DEGs; ‘clusterProfiler’ package(Yu et al. 2012) for Gene Ontology(GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis.

The central concept for designing RNASeqWorkflow is that each step in RNA-Seq analysis is an R function. First users create a RNASeqWorkFlowParam S4 object for all variable checking. Then run the following functions:

1. RNASeqEnvironmentSet_CMD() or RNASeqEnvironmentSet(): Setup RNA-Seq environment.
2. RNASeqQualityAssessment_CMD() or RNASeqQualityAssessment(): Optional. Run quality assessment step.
3. RNASeqQualityTrimming_CMD() or RNASeqQualityTrimming(): Optional. Run quality trimming step.
4. RNASeqReadProcess_CMD() or RNASeqReadProcess(): Run alignment, assembly, quantification, gffcompare and raw reads count generation etc.
5. RNASeqDifferentialAnalysis_CMD() or RNASeqDifferentialAnalysis(): Run differential analysis step.
6. RNASeqGoKegg_CMD() or RNASeqGoKegg(): Do GO and KEGG analysis.

Functions with CMD suffix create an R script and run nohup R CMD BATCH script.R in background. Functions with no CMD suffix process in R shell. After running the above functions, the whole RNA-Seq analysis is done and generated files in each step will be stored in organized file directory. RNASeqWorkflow package makes two-group RNA-Seq analysis more efficient and easier for users.

2 Sample Definition

The sample data that used in this vignette can be downloaded from RNASeqWorkflowData experiment package. The species is Saccharomyces cerevisiae, and reference FASTA and GTF files are downloaded from iGenomes Ensembl R64-1-1. Six FASTQ files are selected from SRP073391 SRA study(SRR3396381, SRR3396382, SRR3396384, SRR3396385, SRR3396386, SRR3396387)(Pang CN et al. 2017). To minimize the workflow process time, each raw FASTQ files are already aligned to BAM files first and reads that are in XV:0-180000 chromsome region are selected and created into new mini FASTQ files. This extracted mini data is just for demonstration purpose.

3 System Requirement

Necessary:

1. R version >= 3.5.0
2. Operating System: ‘Linux’ and ‘macOS’ are supported in RNASeqWorkflow package. ‘Windows’ is not supported. (Because ‘StringTie’ and ‘HISAT2’ are not available for ‘Windows’)
3. During ‘Environment Setup’ step, you can choose whether you want to skip the installation of ‘HISAT2’, ‘StringTie’, ‘Gffcompare’ and ‘SAMtools’. If you want to skip certain program installation, please make sure the command of program that you skip is available by running system2() through R shell.
4. By default, ‘HISAT2’, ‘StringTie’ and ‘Gffcomapre’ binaries will be installed based on your operating system.
5. C Environment for ‘SAMtools’. Different from ‘HISAT2’, ‘StringTie’ and ‘Gffcomapre’, ‘SAMtools’ source code will be installed and compiled rather than binaries. More details about SAMtools requirement.
6. ‘HISAT2’ indexex: Users are advised to provide ‘indices/’ directory in ‘inputfiles/’. ‘HISAT2’ requires at least 160 GB of RAM and several hours to index the entire human genome.

Recommended:

1. Python: Python2 or Python3.
2. 2to3: If the Python version in the system is 3, then this command will be used in the following analysis step. Generally, 2to3 is available if Python3 is available. (Python and 2to3 is used for creating raw reads count for DESeq2 and edgeR. If these commands are not available, DESeq2 and edgeR analysis will be skipped.)

4 “RNASeqWorkFlowParam” S4 Object Creation

This is the pre-step of RNA-Seq workflow in this package. Before starting RNA-Seq analysis, you have to run the constructor function RNASeqWorkFlowParam() and create RNASeqWorkFlowParam S4 object first. This object, which stores checked parameters, will be used as input parameter in the following analysis function.

4.1 RNASeqWorkFlowParam Slots Explanation

There are 11 slots in RNASeqWorkFlowParam:

1. os.type : The operating system type. Value is linux or osx. This package only support ‘Linux’ and ‘macOS’ (no ‘Windows’). If other operating system is detected, ERROR will be reported.
2. python.variable : Python-related variable. Value is a list of whether Python is available and Python version (TRUE or FALSE, 2 or 3).
3. python.2to3 : Availability of 2to3 command. Value is TRUE or FALSE.

4. path.prefix : Path prefix of ‘gene_data/’, ‘RNASeq_bin/’, ‘RNASeq_results/’, ‘Rscript/’ and ‘Rscript_out/’ directories. It is recommended to create a new directory with out any file inside and all the following RNA-Seq results will be installed in it.

5. input.path.prefix : Path prefix of ‘input_files/’ directory. User have to prepare an ‘input_file/’ directory with the following rules:
   • genome.name.fa : reference genome in FASTA file formation.
   • genome.name.gtf : gene annotation in GTF file formation.
   • raw_fastq.gz/: directory storing FASTQ files.
     • Support paired-end reads files only.
     • Names of paired-end FASTQ files : ’sample.pattern_1.fastq.gz’ and ’sample.pattern_2.fastq.gz’. sample.pattern must be distinct for each sample.
   • phenodata.csv : information about RNA-Seq experiment design.
     • First column : Distinct ids for each sample. Value of each sample of this column must match sample.pattern in FASTQ files in ‘raw_fastq.gz/’. Column names must be ids.
     • Second column : independent variable for the RNA-Seq experiment. Value of each sample of this column can only be parameter case.group and control.group. Column name is parameter independent.variable.
   • indices/ : directory storing HT2 indices files for HISAT2 alignment tool.
     • This directory is optional. HT2 indices files corresponding to target reference genome can be installed at HISAT2 official website. Providing HT2 files can accelerate the subsequent steps. It is highly advised to install HT2 files.
     • If HT2 index files are not provided, ‘input_files/indices/’ directory should be deleted.
6. genome.name : Variable of genome name defined in this RNA-Seq workflow (ex. ‘genome.name.fa’, ‘genome.name.gtf’)
   
7. sample.pattern : Regular expression of paired-end fastq.gz files under ‘input_files/raw_fastq.gz/’. IMPORTANT!! Expression shouldn’t have _[1,2].fastq.gz in end.
8. independent.variable: Independent variable for the biological experiment design of two-group RNA-Seq workflow.
9. case.group : Group name of the case group.
10. control.group : Group name of the control group.

11. indices.optional : logical value whether ‘input_files/indices/’ is exit. Value is TRUE or FALSE

4.2 RNASeqWorkFlowParam Constructor Checking Steps

1. Create a new directory for RNA-Seq analysis. It is highly recommended to create a new directory without any files inside. The parameter path.prefix of RNASeqWorkFlowParam() constructor should be the absolute path of this new directory. All the RNA-Seq related files that generated in the following steps will be stored inside of this directory.

2. Create valid ‘input_files/’ directory. You should create a file directory named ‘input_files/’ with neccessary files inside. It should follow the rules metioned above.

3. Run constructor of RNASeqWorkFlowParam S4 object. This constructor will check the validity of input parameters before creating S4 objects.
   • Operating system
   • Python version
   • 2to3 command
     
   • Structure, contents and rules of ‘inputfiles/’
   • Validity of input parameters

4.3 Example

library(RNASeqWorkflow)
library(RNASeqWorkflowData)

input_files.path <- system.file("extdata/", package = "RNASeqWorkflowData")
rnaseq_result.path <- "/tmp/yeast_example/"
dir.create(rnaseq_result.path)
list.files(input_files.path, recursive = TRUE)

##  [1] "input_files/Saccharomyces_cerevisiae_XV_Ensembl.fa" 
##  [2] "input_files/Saccharomyces_cerevisiae_XV_Ensembl.gtf"
##  [3] "input_files/phenodata.csv"                          
##  [4] "input_files/raw_fastq.gz/SRR3396381_XV_1.fastq.gz"  
##  [5] "input_files/raw_fastq.gz/SRR3396381_XV_2.fastq.gz"  
##  [6] "input_files/raw_fastq.gz/SRR3396382_XV_1.fastq.gz"  
##  [7] "input_files/raw_fastq.gz/SRR3396382_XV_2.fastq.gz"  
##  [8] "input_files/raw_fastq.gz/SRR3396384_XV_1.fastq.gz"  
##  [9] "input_files/raw_fastq.gz/SRR3396384_XV_2.fastq.gz"  
## [10] "input_files/raw_fastq.gz/SRR3396385_XV_1.fastq.gz"  
## [11] "input_files/raw_fastq.gz/SRR3396385_XV_2.fastq.gz"  
## [12] "input_files/raw_fastq.gz/SRR3396386_XV_1.fastq.gz"  
## [13] "input_files/raw_fastq.gz/SRR3396386_XV_2.fastq.gz"  
## [14] "input_files/raw_fastq.gz/SRR3396387_XV_1.fastq.gz"  
## [15] "input_files/raw_fastq.gz/SRR3396387_XV_2.fastq.gz"

Check the files in ‘inputfiles/’ directory.

exp <- RNASeqWorkflowParam(path.prefix = rnaseq_result.path, 
input.path.prefix = input_files.path, 
genome.name = "Saccharomyces_cerevisiae_XV_Ensembl", 
sample.pattern = "SRR[0-9]*_XV",
independent.variable = "state", 
case.group = "60mins_ID20_amphotericin_B", 
control.group = "60mins_ID20_control")

In this example, RNASeqWorkFlowParam S4 object is store in exp for subsequent RNA-Seq analysis steps. Any ERROR occured in checking steps will terminate the program.

5 Environment Setup

This is the first necessary step of RNA-Seq workflow in this package. To set up the environment, run RNASeqEnvironmentSet_CMD() to execute process in background or run RNASeqEnvironmentSet() to execute process in R shell.

5.1 Files Setup

1. Create Base Directories. ‘gene_data/’, ‘RNASeq_bin/’, ‘RNASeq_results’, ‘Rscript’ and ‘Rscript_out’ will be created under path.prefix directory. Here are the usage of these five main directories:
   • ‘gene_data/’: Symblic links of ‘input_files/’ and files that are created in each step of RNA-Seq analysis will be stored in this directory.
     
   • ‘RNASeq_bin/’: The binaries of necessary tools, HISAT2, SAMtools, StringTie and Gffcompare, are installed in this directory.
   • ‘RNASeq_results’: The RNA-Seq results, for example, alignment results, quality assessment results, differential analysis results etc., will be stored in this directory.
   • ‘Rscript’: If your run XXX_CMD() function, corresponding R script(XXX.R) for certain step will be created in the directory.
   • ‘Rscript_out’: The corresponding output report for R script(XXX.Rout) will be stored in this directory.
2. Symbolic links will be created from files in ‘input_files/’ to path.prefix directory.

5.2 Necessary Tools Installation

The operating system of your workstation will be detected. If the operating system is other than Linux and macOS, ERROR will be reported. You can choose whether you want to install programs automatically or not. Four programs, HISAT2, SAMtools, StringTie and Gffcompare, are necessary for the following analysis workflow. In RNASeqEnvironmentSet_CMD() and RNASeqEnvironmentSet() functions, there are four parameters, install.hisat2, install.stringtie, install.gffcompare, and install.samtools, that you can set to determin whether particular softwares are going to be installed automatically or not. Defualt values are TRUE and these four programs will be installed by default. If you want to skip the installation process of particular program, you can set the parameter value to FALSE. But please make sure that the program you skipped in installation process is available by running system2() through R shell. Availability of these commands will be checked at the end of this environment setup function. Any unavailability of the commands will cause fail in ‘Environment Setup’ step.

If the parameter values are TRUE, these four tools will be installed: * HISAT2 * Based on your operating system, hisat2-2.1.0-Linux_x86_64.zip or hisat2-2.1.0-OSX_x86_64.zip zipped file will be installed. * Installed file will be unzipped and all binary files will be copied under ‘RNASeq_bin/’ * SAMtools * samtools-1.8.tar.bz2 source files will be installed. * Installed files will be unzipped and compiled. If there is any ERROR occurred during compilation, program will be stopped. You should check whether the development environment is OK for SAMtools before rerunning RNASeqEnvironmentSet_CMD or RNASeqEnvironmentSet() to set up the environment again. * Maked SAMtools binary will be copied under ‘RNASeq_bin/’. * StringTie * Based on your operating system, stringtie-1.3.4d.Linux_x86_64.tar.gz or stringtie-1.3.4d.Linux_x86_64 zipped file will be installed. * Installed file will be unzipped and all binary files will be copied under ‘RNASeq_bin/’. * Gffcompare * Based on your operating system, gffcompare-0.10.4.Linux_x86_64.tar.gz or gffcompare-0.10.4.Linux_x86_64.tar.gz zipped file will be installed. * Installed file will be unzipped and all binary files will be copied under ‘RNASeq_bin/’.

5.3 Export Path

‘RNASeq_bin/’ will be added to R PATH so that these binaries can be found in R environment. In the last step of environment setting, hisat2 --version,stringtie --version,gffcompare --version,samtools --version commands will be checked in order to make sure the environment is correctly constructed.

5.4 Example

Run RNASeqEnvironmentSet_CMD() or RNASeqEnvironmentSet(). 1. Run in background Result will be reported in ‘Rscript_out/Environment_Set.Rout’. Make sure the environment is successfully set up before running the subsequent steps.

RNASeqEnvironmentSet_CMD(exp)

2. Run in R shell Result will be reported in R shell. Make sure the environment is successfully set up before running the subsequent steps.

RNASeqEnvironmentSet(exp@path.prefix, 
                     exp@input.path.prefix, 
                     exp@genome.name, 
                     exp@sample.pattern, 
                     exp@indices.optional, exp@os.type)

6 FASTQ Files Quality Assessment

This is an optional step of RNA-Seq workflow in this package. However, it is strongly recommended to do it before processing reads. To assess the quality of raw reads in FASTQ files, run RNASeqQualityAssessment_CMD() to execute process in background or run RNASeqQualityAssessment() to execute process in R shell.

6.1 “systemPipeR” Quality Assessment

In this process, systemPipeR package is used for quality assessment. Here are the following steps:

1. Check the number of times that user has run quality assessment process and create the corresponding files ‘RNASeq_results/QA_results/QA_{times}’.
2. RNA-Seq environment set up. ‘rnaseq/’ directory will be created by systemPipeR package.
3. Create ‘data.list.txt’ file.
4. Reading FASTQ files and create ‘fastqReport.pdf’ as the report result of quality assessment

5. Remove ‘rnaseq/’ directory.

   This quality assessment result is generated by systemPipeR package. It will be stored as PDF.

6.2 Example

Run RNASeqQualityAssessment_CMD() or RNASeqQualityAssessment(). 1. Run in background Result will be reported in ‘Rscriptout/Quality_Assessment.Rout’. Make sure quality assessment is successfully done before running the subsequent steps.

RNASeqQualityAssessment_CMD(exp)

2. Run in R shell Result will be reported in R shell. Make sure quality assessment is successfully done before running the subsequent steps.

RNASeqQualityAssessment(exp@path.prefix,
                        exp@input.path.prefix,
                        exp@sample.pattern)

## [1] "Generated rnaseq directory. Next run in rnaseq directory, the R code from *.Rmd (*.Rnw) template interactively. Alternatively, workflows can be exectued with a single command as instructed in the vignette."

7 FASTQ Files Quality Trimming

This is an optional step of RNA-Seq workflow in this package. However, it is recommended to do it if the result of quality assessment is unsatisfied. To trim the raw reads in FASTQ files, run RNASeqQualityTrimming_CMD() to execute process in background or run RNASeqQualityTrimming() to execute process in R shell. After trimming process, it is strongly recommended to redo quality assessment step. Different quality assessment result will be stored in different directories with index of processing order.

7.1 “ShortRead” Quality Trimming

In this process, ShortRead package is used for quality trimming. Followings are trimming steps and method explanation.

1. FASTQ files with sample.pattern in ‘gene_data/raw_fastq.gz’ will be listed and be check. This trimming function only support paired-end FASTQ files.
2. The probability of error per each base pair is calculated by Phred Quality in FASTQ files and the cumulative probability of error of each base pair will also be calculated. The threshold cumulative probability cum.error, with default value 1, is used to find the trimming point of paired end files.
3. reads.length.limit, with default value 36, determines the minimum base pair length of reads. Length of reads less than reads.length.limit will be filtered out.
4. Untrimmed original FASTQ files are moved to ‘gene_data/raw_fastq.gz/original_untrimmed_fastq.gz’, and trimmed reads are written to ‘gene_data/raw_fastq.gz/’
5. It is strongly recommended to run RNASeqQualityAssessment_CMD() or RNASeqQualityAssessment() for quality assessment after quality trimming.

7.2 Example

Run RNASeqQualityTrimming_CMD() or RNASeqQualityTrimming(). 1. Run in background Result will be reported in ‘Rscript_out/Quality_Trimming.Rout’. Make sure quality trimming is successfully done before running the subsequent steps.

RNASeqQualityTrimming_CMD(exp)

2. Run in R shell Result will be reported in R shell. Make sure quality assessment is successfully done before running the subsequent steps.

RNASeqQualityTrimming(exp@path.prefix,
                      exp@sample.pattern)

8 Reads Process (Alignment, Assembly, Gffcompare, Quantification)

This is a second necessary step of RNA-Seq workflow in this package. Programs installed in environment setting step, HISAT2, SAMtools, StringTie and Gffcompare, will be executed. To process raw reads FASTQ files, run RNASeqRawReadProcess_CMD() to execute process in background or run RNASeqRawReadProcess() to execute process in R shell. For more details about commands that executed during each reads processing step, please check the reported ‘RNASeq_results/COMMAND.txt’. The process steps are mainly refering to this paper(Pertea et al. 2016).

8.1 HISAT2 Index

In pre-step(RNASeqWorkFlowParam creation step), ‘indices/’ directory is checked and the whether HT2 indices files are provided is known. If not provided, following commands will be executed:

• Input: ‘genome.name.gtf’, ‘genome.name.fa’
• Output: ‘genome.name.ss’, ‘genome.name.exon’, ’genome.name_tran.{number}.ht2’

1. extract_splice_sites.py, extract_exons.py execution
   • These two commands are executed to extract splice-site and exon information from gene annotation file.
2. hisat2-build index creation
   • This command build HISAT2 indices files with genome.name.ss and genome.name.exon created in the previous step. Be aware that index building step requires a larger amount of memory and longer time than steps, and it might not be possible to run on some personal workstations. It is highly recommended to check the availibility of HT2 indices files at HISAT2 official website for your target reference genome beforehands. Install HT2 indices files will greatly shorten the analysis time.
3. Write ‘RNASeq_results/COMMAND.txt’
   • Shell command that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.2 HISAT2 Alignment

• Input: ’genome.name_tran.{number}.ht2’, ‘sample.pattern.fastq.gz’
• Output: ‘sample.pattern.sam’

1. hisat2 command is executed on paired end FASTQ files. SAM files will be created.
   • SAM files are stored in ‘gene_data/raw_bam/’.
2. Write ‘RNASeq_results/COMMAND.txt’
   • Shell command that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.3 SAMtools SAM to BAM

• Input: ‘sample.pattern.sam’
• Output: ‘sample.pattern.bam’

1. samtools command is executed. Output SAM files from HISAT2 will be converted to BAM files.
   • BAM files are stored in ‘gene_data/raw_sam/’.
2. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.4 StringTie Assembly

• Input: ‘genome.name.gtf’, ‘sample.pattern.bam’
• Output: ‘sample.pattern.gtf’

1. stringtie command is executed.
   • Assembler of RNA-Seq alignments into potential transcripts.
   • Output assembled GTF files which are from each FASTQ files are stored in ‘gene_data/raw_gtf/’
2. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.5 StringTie Merge GTF

• Input: ‘sample.pattern.gtf’
• Output: ‘stringtiemerged.gtf’, ‘mergelist.txt’

1. Creating mergelist.txt
   • gene_data/merged/mergelist.txt is created for the merging step.
2. stringtie command is executed.
   • Transcript merger merges each sample.pattern.gtf into stringtiemerged.gtf
   • Output files are all stored in ‘gene_data/merged/’
3. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.6 Gffcompare

• Input: ‘genome.name.gtf’, ‘stringtie_merged.gtf’
• Output: ‘merged.annotated.gtf’, ‘merged.loci’, ‘merged.stats’, ‘merged.stringtie_merged.gtf.refmap’, ‘merged.stringtie_merged.gtf.tmap’, ‘merged.tracking’

1. gffcompare command is executed.
   • The comparison result of merged GTF file and reference annotation file is reported under ‘merged/’ directory.
2. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.7 StringTie Creates Ballgown Input Directories

• Input: ‘stringtie_merged.gtf’
• Output: ‘ballgown/’, ‘gene_abundance/’

1. stringtie command is executed.
   • StringTie will create the input directory for ballgown package for the following differential analysis. Ballgown-related files will be stored by each sample name in ‘gene_data/ballgown/’.
   • StringTie will store gene-related information in TSV file by each sample name in ‘gene_data/gene_abundance/’.
2. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.8 Raw Reads Count Table Creation

Whether this step is executed depends on the availability of Python on your workstation.

• Input: ‘samplelst.txt’
• Output: ‘gene_count_matrix.csv’, ‘transcript_count_matrix.csv’

1. Reads count table converter Python script is downloaded as prepDE.py
2. Python checking
   • If Python is not available, this step is skipped.
   • If Python2 is available, prepDE.py is executed.
   • If Python3 is available, 2to3 command will be checked.(Usally, if Python3 is installed, 2to3 command will be installed too.)
   • If Python3 is available but 2to3 command is unavailable, raw reads count generation step will be skipped.
   • If Python3 and 2to3 command is available, prepDE.py is converted to file that can be executed by Python2 and be executed.
3. raw reads count creation
   • Raw reads count is created and the results are stored in ‘gene_data/reads_count_matrix/’
4. Write ‘RNASeq_results/COMMAND.txt’
   • Shell commands that run in this step will be documented into ‘RNASeq_results/COMMAND.txt’.

8.9 Example

Run RNASeqReadProcess_CMD() or RNASeqReadProcess(). 1. Run in background Result will be reported in ‘Rscriptout/Raw_Read_Process.Rout’. Make sure raw read process is successfully done before running the subsequent steps.

RNASeqReadProcess_CMD(exp)

2. Run in R shell Result will be reported in R shell. Make sure raw read process is successfully done before running the subsequent steps.

python.variable <- exp@python.variable
python.variable.answer <- python.variable$check.answer
python.variable.version <- python.variable$python.version
RNASeqReadProcess(exp@path.prefix, 
                  exp@input.path.prefix, 
                  exp@genome.name, 
                  exp@sample.pattern, 
                  python.variable.answer, 
                  python.variable.version, 
                  exp@python.2to3, 
                  num.parallel.threads = 10, 
                  exp@indices.optional)

## [1] "SRR3396381_XV /tmp/yeast_example/gene_data/ballgown/SRR3396381_XV/SRR3396381_XV.gtf"

9 Differential Expression Analysis

This is the third necessary step of RNA-Seq workflow in this package. To do differential expression analysis, run RNASeqDifferentialAnalysis_CMD() to execute process in background or run RNASeqDifferentialAnalysis() to execute process in R shell. In RNASeqWorkflow package, we provide four ways to do differential expression analysis: ballgown, TPM & Student t-test, DESeq2 and edgeR. Gene ids reported by StringTie(raw reads count) and ballgown(result) will be mapped to ‘gene_name’ provided in GTF file. ‘gene_name’ will be used to do gene functional analysis and pathway analysis in the next step.(!!!Functional 還有一些問題～ 這週要和學姊討論一下!!!)

9.1 Alignment Report

If you run RNASeqRawReadProcess_CMD() in the previous step, ‘Rscript_out/Read_Process.Rout’ will be created and in the beginning of this step, alignment results, aCSV file and a PNG file, will be reported based on ‘Rscript_out/Read_Process.Rout’ file.

9.2 General Visualization

‘ballgown’, ‘TPM & Student t-test’, ‘DESeq2’ and ‘edgeR’ will be processed in order in this step. After doing differential expression analysis, results of each method will be visualized and stored in ‘RNASeq_results/’. The pictures showed in the following steps are the visualization result of data in RNASeqWorkflowData experiment package. The detail steps and the method-specific plots in each method will be discussed sperately later. Here we focus on the some general plots that will be created and stored in the directories of these four differential analysis tools. We only show ballgown result plots as an example. The following are the general plots(ballgown):

• Pre differential analysis sample profile related plot:
  • Frequency Plots:

    • ‘Frequency_Plot_normalized_count_ggplot2.png’ The frequency of normalized reads count(In ballgown is FPKM). The range of x axis(FPKM) is from 0% of data minus 20(smallest FPKM values - 20) to 90% of data plus 20. It is drawn by ggplot2.

    • ‘Frequency_Plot_log_normalized_count_ggplot2.png’ The frequency of log base two of normalized reads count plus one(in ballgown is log₂(FPKM+1)). The range of x axis(log₂(FPKM+1)) is from 0% of data minus 5(smallest log₂(FPKM+1) - 5) to 99% of data plus 5. It is drawn by ggplot2.

  • Distribution Plots:

    • ‘Box_Plot_ggplot2.png’ The distribution of log base two of normalized reads count plus one(in ballgown is log₂(FPKM+1)) depicting groups of numerical . Case is on left-hand side in turquoise and control is on right-hand side in yellow. It is drawn by ggplot2.

    • ‘Violin_Plot_ggplot2.png’ The distribution of log base two of normalized reads count plus one(in ballgown is log₂(FPKM+1)) of each sample. Case is on left-hand side in green and control is on right-hand side in yellow. It is drawn by ggplot2.

  • PCA Plots: In principal component analysis(PCA) test before differential expression analysis, we use FactoMineR package to calculate principal component scores and factoextra to extract and visualize the results of principal component scores. Three plots are provided.

    • ‘Dimension_PCA_Plot_factoextra.png’ Percentage of explained variances in top five dimensions are plotted. The top two percentage of explained variance will be used to plot PCA plot.

    • ‘PCA_Plot_factoextra.png’ This PCA plot is drawn by fviz_pca_ind() function in factoextra. Case group is in green and control group is in yellow. The smaller points represent the individual samples and the bigger points represent the average of case group samples and control group samples. Ellipses will be added for case and control group samples.

    • ‘PCA_Plot_ggplot2.png’ This PCA plot is drawn directly by ggplot2 with principal component scores calculated by FactoMineR package. Case group is in green and control group is in yellow.

  • Correlation Plot :

    • ‘Correlation_Heat_Plot_ggplot2.png’ Correlations between each pair of samples is calculated by using R Stats package and drawn by ggplot2. Maximum correlation(1) is in red and minimum correlation in all sample pairs is in blue.

    • ‘Correlation_Dot_Plot_corrplot.png’ Correlation dot plot is drawn by corrplot() function in corrplot package. Maximum correlation(1) is in red and minimum correlation in all sample pairs is in blue.

    • ‘Correlation_Bar_Plot_PerformanceAnalytics.png’ Correlation bar plot is drawn by chart.Correlation() function in PerformanceAnalytics package.

• Differential analysis related plot
  • Volcano Plot :
    • ‘Volcano_Plot_graphics.png’ Volcano plot is drawn by ggplot2. X axis is log base two of fold change(log₂(fold change)); Y axis is negative log base ten of p-value(log₁₀(p-value)). The upregulated and downregulated genes are colored with red and green.
  • PCA Plot : After differential expression(DE) analysis, principal component analysis is done again. Same as pre-DE PCA, we use FactoMineR package to calculate principal component scores and factoextra to extract and visualize the results of principal component scores. Three plots are provided.

    • ‘Dimension_PCA_Plot_factoextra.png’

    • ‘PCA_Plot_factoextra.png’

    • ‘PCA_Plot_ggplot2.png’

  • Heatmap Plot :
    • ‘Heatmap_Plot_pheatmap.png’ Heatmap is drawn by pheatmap() function in pheatmap package. Log base 2 of normalized count plus one(log₂(FPKM+1) for ballgown) is calculated first. All calculated sample values substract mean of case group sample values(log₂(case_mean_FPKM+1) - log₂(each_FPKM+1) ) will be used to drawn this heatamp. Case group samples are on the left in green and control group samples are on the right in yellow.

9.3 “ballgown” Analysis

Ballgown is designed to simplify differential expression analysis of RNA-Seq data(more information in ballgown github). The default statistical model is a parametric F-test comparing nested linear models. Normalized row count in ballgown is represented as FPKM values. The following are ballgown analysis steps:

• Input: ‘gene_data/ballgown/’
• Output: ‘RNASeq_results/ballgown_analysis/’

1. ballgown object is created and written in ‘RNASeqresults/ballgownanalysis/ballgownRobject/ballgown.rda’.
2. The row sum of sample FPKM values equals 0 will be filter out. Moreover, row of extracted statistic results from created ballgown object with pval equals 0 or qval equals 0 will be filterd out too. Log base two fold change(log₂(fold change)) value will be calculated as column name log2FC. According to the ‘gene_data/phenodata.csv’ file, normalized count will be seperated into case and control group with column name sample.pattern.FPKM.
3. A report CSV file combined with normalized FPKM count, average of case group, control group and statistic results will be stored as ‘RNASeq_results/ballgown_analysis/ballgown_normalized_result.csv’.
4. Differential expressed gene result ‘RNASeq_results/ballgown_analysis/ballgown_normalized_DE_result.csv’ is reported. Default selecting threshold is pval < 0.05 and log2FC > 1 | log2FC < 1.
5. Visualization. The general plots mentioned above and the following plot will be drawn.
   • Differential analysis related plot
     • MA Plot : The plot visualizes the differences between case and control by transforming the data onto X axis log2FC and Y axis log2(FPKM.mean). It is drawn by ggplot2 package.

9.4 TPM & Student t-test Analysis

Normalized count is represented as TPM values. The statistic model used here is Student’s t-test. The following are analysis steps:

• Input: ‘gene_data/ballgown/’
• Output: ‘RNASeq_results/TPM_analysis/’

1. Calculate TPM values from ballgown FPKM values and save with column name sample.pattern.FPKM FPKM values.
2. Use Student’s t-test to calculate p-value and saved as column name pval.
3. Calculate fold change values as dividing average TPM values of control group plus one by average TPM values of case group plus one((Average_case_TPM+1) / (Average_control_TPM+1) ) with column name fc. Log base two fold change(log₂(fold change)) is also calculated and stored as log2FC.
4. A report CSV file combined with normalized TPM count, average of case group, control group and statistic results will be stored as ‘RNASeq_results/TPM_analysis/TPM_normalized_result.csv’.
5. Differential expressed gene result ‘RNASeq_results/TPM_analysis/TPM_normalized_DE_result.csv’ is reported. Default selecting threshold is pval < 0.05 and log2FC > 1 | log2FC < 1
6. Visualization. The general plots mentioned above and the following plot will be drawn.
   • Differential analysis related plot
     • MA Plot : The plot visualizes the differences between case and control by transforming the data onto X axis log2FC and Y axis log2(TPM.mean). It is drawn by ggplot2 package.

9.5 “DESeq2” Analysis

DESeq2 estimate variance-mean dependence in raw reads count( more information inDESeq2 document). The statistic model for differential expression is using negative binomial distribution. DESeq2 package takes sequence depth and RNA composition into consideration and use median of ratios normalization(MRN) method to reads count normalization (related site). The follwing are the analysis steps.

• Input: ‘gene_data/reads_count_matrix/gene_count_matrix.csv’ (Row sum of gene raw reads count that equals o will be filtered out)
• Output: ‘RNASeq_results/ballgown_analysis/’

1. Create DESeqDataSet object from raw reads count of genes by running DESeqDataSetFromMatrix() function in DESeq2.
2. Run DESeq2() function to do differential expression analysis.
3. A report CSV file combined with normalized MRN count, average of case group, control group and statistic results will be stored as ‘RNASeq_results/DESeq2_analysis/DESeq2_normalized_result.csv’.
4. Differential expressed gene result ‘RNASeq_results/DESeq2_analysis/DESeq2_normalized_DE_result.csv’ is reported. Default selecting threshold is pval < 0.05 and log2FC > 1 | log2FC < 1
5. Visualization. The general plots mentioned above and the following plots will be drawn.
   • Pre differential analysis sample profile related plot:
     • Dispersion Plot : DESeq2 provides a useful function, plotDispEsts(), to plot the dispersion estimates. X axis is mean of normalized counts; Y axis is dispersion.
   • Differential analysis related plot
     • MA Plot : The plot visualizes the differences between case and control by transforming the data onto X axis mean of normalized counts and Y axis log fold change. It is created by plotMA() function in DESeq2.

9.6 “edgeR” Analysis

edgeR, a tool for differential expression analysis, implements a range of statistical method based on negative binomial distribution. edgeR package normalizes for RNA composition by finding a set of scaling factors for the library sizes, which is computed by trimmed mean of M-values(TMM). Normalized count is represented as cpm with library size scaled by TMM (related site). The following are analysis steps:

• Input: ‘gene_data/reads_count_matrix/gene_count_matrix.csv’ (Row sum of gene raw reads count that equals o will be filtered out)
• Output: ‘RNASeq_results/edgeR_analysis/’

1. Create DEGList object from raw reads count by running DGEList() function.
2. Normalize DEGList object with TMM by running calcNormFactors() function.
3. Run estimateCommonDisp() to maximize the negative binomial conditional common likelihood and then run estimateTagwiseDisp() to estimate tagwise dispersion values by an empirical Bayes method.
4. Compute genewise exact tests and get statistic results by running exactTest().
5. Get normalized count by running cpm() on TMM scaled DEGList object.
6. A report CSV file combined with normalized TMM count, average of case group, control group and statistic results will be stored as ‘RNASeq_results/edgeR_analysis/edgeR_normalized_result.csv’.
7. Differential expressed gene result ‘RNASeq_results/edgeR_analysis/edgeR_normalized_DE_result.csv’ is reported. Default selecting threshold is pval < 0.05 and log2FC > 1 | log2FC < 1

8. Visualization. The plots mentioned above and the following plots will be drawn.

   • Pre differential analysis sample profile related plot

     • MeanVar Plot edgeR provides a useful function plotMeanVar() to visualize the mean-variance relationship in DEGList.

     • BCV Plot edgeR provides a useful function plotBCV() to plot the genewise biological coefficient of variance(BCV) against gene abundace.

     • MDS Plot edgeR provides a useful function plotMDS.DGEList() to plot samples on a 2D scatterplot with distances representing expression differences between the samples.

9.7 Example

Run RNASeqDifferentialAnalysis_CMD() or RNASeqDifferentialAnalysis(). 1. Run in background Result will be reported in ‘Rscriptout/Differential_Analysis.Rout’. Make sure differential expression analysis is successfully finished before running subsequent steps.

RNASeqDifferentialAnalysis_CMD(exp)

2. Run in R shell Result will be reported in R shell. Make sure differential expression analysis is successfully finished before running subsequent steps.

RNASeqDifferentialAnalysis(exp@path.prefix,
                           exp@genome.name,
                           exp@sample.pattern,
                           exp@independent.variable,
                           exp@case.group,
                           exp@control.group, 
                           ballgown.pval = 0.05, 
                           ballgown.log2FC = 1, 
                           TPM.pval = 0.05, 
                           TPM.log2FC = 1, 
                           DESeq2.pval = 0.1, 
                           DESeq2.log2FC = 1, 
                           edgeR.pval = 0.05, 
                           edgeR.log2FC = 1)

10 Functional Analysis (Gene Ontology) and Pathway Analysis (Kyoto Encyclopedia of Genes and Genomes)

這邊還有點問題～週五想和學姊討論一下～ 這邊先不用看！～～ This is the third optional step of RNA-Seq workflow in this package. clusterProfiler is used in this step. Gene Ontology(GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis are made based on the differential expressed genes(DEG) that are found in four different differential analyses, ballgown, TPM&t-test, DESeq2 and edgeR. Run RNASeqGoKegg_CMD() to execute process in background or run RNASeqGoKegg() to execute process in R shell.

The gene names used in StringTie and ballgown are in the type SYMBOL in clusterProfiler. In GO functional analysis and KEGG pathway analysis, SYMBOL ID type will be converted into ENTREZID ID type by bitr() function in clusterProfiler first. Those SYMBOL with no corresponding ENTREZID will return NA and be filtered out. The genes with Inf or -Inf log2 fold change will be filtered out too. ID conversion will be done in each differential analysis tools, ballgown, TPM & Student’s t-test, DESeq2 and edgeR.

10.1 Gene Ontology

Gene Ontology is the framework for the model of biology. It defines the universe of concepts relating to gene functions(GO terms) along three aspects: molecular function(MF), cellular component(CC), biological process(BP), and how these functions are related to each other(relation) (GO website). In this process, GO gene set enrichment analysis, GO classification and GO over-representation test are implemented by clusterProfiler package.

10.1.1 GO Gene Set Enrichment Analysis(GSEA)

GO GSEA is implemented by running gseGO() function in clusterProfiler. As mentioned in the vignette of clusterProfiler, this approach aggregates the statistic of each gene within gene set, thus make it possible to detect the situation that genes where the difference is small, but evidenced in coordinated way in a set of related genes.

In this step, the input gene set is all genes reported in each differential analysis tools(not just for selected differential expressed genes). If enriched genes are found, the top 5 genes with smallest p-value will be plotted with the function gseaplot() in clusterProfiler. If not, ‘GO_GSE_NO_TERM’ empty file will be created.

10.1.2 GO Classification

The input gene set is differential genes that are reported in each differential analysis tools. groupGO() function in clusterProfiler is used. Classification result will be stored in CSV. The result of classification will be visualized. 12 highest GeneRatio functional description will be selected and plotted as bar plot.

10.1.3 GO Over-representation

The input gene set is differential genes that are reported in each differential analysis tools. enrichGO() function in clusterProfiler is used. GO over-representation result will be stored in CSV. The result of GO over-representation will be visualized as bar plot and dot plot.

10.2 Kyoto Encyclopedia of Genes and Genomes

Kyoto Encyclopedia of Genes and Genomes(KEGG) is a database resource for understanding functions and utilities of the biological system from molecular-level information (KEGG website). In this process, KEGG gene set enrichment analysis, KEGG over-representation test are implemented by clusterProfiler package.

10.2.1 KEGG Gene Set Enrichment Analysis(GSEA)

KEGG GSEA is implemented by running gseKEGG() function in clusterProfiler. In this step, the input gene set is all genes reported in each differential analysis tools(not just for selected differential expressed genes). If enriched genes are found, the top 5 genes with smallest p-value will be plotted with the function gseaplot() in clusterProfiler. If not, ‘KEGG_GSE_NO_TERM’ empty file will be created.

10.2.2 KEGG Over-representation

The input gene set is differential genes that are reported in each differential analysis tools. enrichKEGG() function in clusterProfiler is used. KEGG over-representation result will be stored in CSV. The result of KEGG over-representation will be visualized as bar plot and dot plot.

10.3 Example

1. Run in background Result will be reported in ‘Rscriptout/GO_KEGG_Analysis.Rout’.

RNASeqGoKegg_CMD(exp, 
                 OrgDb.species = "org.Sc.sgd.db",
                 go.level = 3, 
                 input.TYPE.ID = "GENENAME",
                 KEGG.organism = "sce")

2. Run in R shell Result will be reported in R shell.

RNASeqGoKegg(exp@path.prefix, 
             exp@independent.variable, 
             OrgDb.species = "org.Sc.sgd.db", 
             go.level = 3, 
             input.TYPE.ID = "GENENAME",
             KEGG.organism = "sce")

11 Conlusion

12 Session Information

toLatex(sessionInfo())

## \begin{itemize}\raggedright
##   \item R version 3.5.0 (2018-04-23), \verb|x86_64-pc-linux-gnu|
##   \item Locale: \verb|LC_CTYPE=en_US.UTF-8|, \verb|LC_NUMERIC=C|, \verb|LC_TIME=en_US.UTF-8|, \verb|LC_COLLATE=C|, \verb|LC_MONETARY=en_US.UTF-8|, \verb|LC_MESSAGES=en_US.UTF-8|, \verb|LC_PAPER=en_US.UTF-8|, \verb|LC_NAME=C|, \verb|LC_ADDRESS=C|, \verb|LC_TELEPHONE=C|, \verb|LC_MEASUREMENT=en_US.UTF-8|, \verb|LC_IDENTIFICATION=C|
##   \item Running under: \verb|Ubuntu 17.10|
##   \item Matrix products: default
##   \item BLAS: \verb|/usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1|
##   \item LAPACK: \verb|/home/kuan-hao/anaconda3/lib/libmkl_rt.so|
##   \item Base packages: base, datasets, grDevices, graphics, methods,
##     parallel, stats, stats4, utils
##   \item Other packages: AnnotationDbi~1.43.1, Biobase~2.41.2,
##     BiocGenerics~0.27.1, BiocStyle~2.9.6, IRanges~2.15.16,
##     RNASeqWorkflow~0.99.0, RNASeqWorkflowData~0.1.0,
##     S4Vectors~0.19.19, edgeR~3.23.3, ggplot2~3.0.0, limma~3.37.3,
##     org.Hs.eg.db~3.6.0, pathview~1.21.0
##   \item Loaded via a namespace (and not attached):
##     AnnotationFilter~1.5.2, AnnotationForge~1.23.3, BBmisc~1.11,
##     BSgenome~1.49.3, BatchJobs~1.7, BiocManager~1.30.1,
##     BiocParallel~1.15.8, Biostrings~2.49.1, Category~2.47.1,
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13 Reference

https://www.rna-seqblog.com/introduction-to-rna-sequencing-and-analysis/

https://en.wikipedia.org/wiki/RNA-Seq

Pang CN et al., “Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response.”, Sci Rep, 2017 Jan 12;7:40232

Kim D, Langmead B and Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature Methods 2015

Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT & Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nature Biotechnology 2015, doi:10.1038/nbt.3122

Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods, 5(7), 621.

Backman TWH, Girke T (2016). “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics, 17(1). doi: 10.1186/s12859-016-1241-0, https://doi.org/10.1186/s12859-016-1241-0.

Morgan M, Anders S, Lawrence M, Aboyoun P, Pagès H, Gentleman R (2009). “ShortRead: a Bioconductor package for input, quality assessment and exploration of high-throughput sequence data.” Bioinformatics, 25, 2607-2608. doi: 10.1093/bioinformatics/btp450, http://dx.doi.org10.1093/bioinformatics/btp450.

Fu J, Frazee AC, Collado-Torres L, Jaffe AE, Leek JT (2018). ballgown: Flexible, isoform-level differential expression analysis. R package version 2.12.0.

Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140.

McCarthy, J. D, Chen, Yunshun, Smyth, K. G (2012). “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Research, 40(10), 4288-4297.

Yu G, Wang L, Han Y, He Q (2012). “clusterProfiler: an R package for comparing biological themes among gene clusters.” OMICS: A Journal of Integrative Biology, 16(5), 284-287. doi: 10.1089/omi.2011.0118.

Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature protocols, 11(9), 1650. ISO 690

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.